For closed chemical networks (without inflow and outflow), there are a number of model reduction techniques proposed in the literature. The time-scale separation technique as discussed in [1]}, [2]}, [3]}, [4]} and the quasi steady-state approximation (QSSA) (see, for example, [5]}) are the most commonly used techniques. Hardin in [6]} extends the QSSA approach by considering the higher-order approximation in the computation of the quasi steady-state. In [7]}, [8]} and [9]}, integer optimization techniques are used to determine which components and/or reactions can be eliminated from the original model without a substantial alteration of the model behaviour. Danø et al in [10]} propose a model reduction method by identification and elimination of variables in such a way that the basic dynamic properties of the original model are preserved. In [11]}, model reduction is achieved by simplifying the rate equations of individual enzymes in the network. In [12]}, it is rather the number of parameters of a given model that is reduced. This is done by identifying a region in the parameter space where some of the parameters can be pruned to zero while ensuring the outputs of the reduced model match those of the original model within a given tolerance.
